EIS-OBEA: Enhanced Image Steganalysis via Opposition-Based Evolutionary Algorithm

Recent years have witnessed a spurt progress in steganography, which poses challenges for steganalysis. However, previous steganalysis methods attach equal attention to various feature information, while key feature information in detection is ubiquitously ignored, and the detection time-space cost...

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Published in:IEEE transactions on information forensics and security Vol. 20; pp. 3616 - 3631
Main Authors: Ma, Yuanyuan, Xu, Lige, Zhang, Qianqian, Zhang, Yi, Xin, Xianwei, Luo, Xiangyang
Format: Journal Article
Language:English
Published: IEEE 2025
Subjects:
Accuracy
coefficient for steganalysis
Costs
Data mining
Deep learning
Enhanced steganalysis
evolutionary algorithm
Evolutionary computation
Feature extraction
Lenses
opposition-based learning
Optimization
Steganography
Training
ISSN:1556-6013, 1556-6021
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Abstract Recent years have witnessed a spurt progress in steganography, which poses challenges for steganalysis. However, previous steganalysis methods attach equal attention to various feature information, while key feature information in detection is ubiquitously ignored, and the detection time-space cost is burdened consequently. To alleviate this predicament, this paper proposes an enhanced image steganalysis via opposition-based evolutionary algorithm (EIS-OBEA), which can guide steganalysis showing more solicitude for key feature information and reduce detection time overhead. Specifically, evolutionary algorithm is introduced into enhanced steganalysis. To elevate searching ability for steganalysis key feature submodels, Tent map is applied in enhanced steganalysis population initialization because of its great randomness. Secondly, considering that opposition-based learning can dynamically adjust searching space of enhanced steganalysis population, opposition-based learning via lens imaging strategy is proposed to help enhanced steganalysis escape from local optimal solutions. Then, to reasonably evaluate the detection contribution of steganalysis key feature submodels, the pearson correlation coefficient for steganalysis is designed. On this basis, fitness function is devised to select superior individuals and obtain steganalysis key feature submodels after iteration. It is noted that EIS-OBEA can optimize steganalysis training samples into quite small-size data, so that computational cost can be significantly reduced when maintaining or even improving detection accuracy. Extensive experimental results substantiate that compared with the state-of-the-art peer algorithms, EIS-OBEA not only achieves highly competitive or even better detection performance, but also meliorates steganalysis time-space cost to a large extent.
AbstractList Recent years have witnessed a spurt progress in steganography, which poses challenges for steganalysis. However, previous steganalysis methods attach equal attention to various feature information, while key feature information in detection is ubiquitously ignored, and the detection time-space cost is burdened consequently. To alleviate this predicament, this paper proposes an enhanced image steganalysis via opposition-based evolutionary algorithm (EIS-OBEA), which can guide steganalysis showing more solicitude for key feature information and reduce detection time overhead. Specifically, evolutionary algorithm is introduced into enhanced steganalysis. To elevate searching ability for steganalysis key feature submodels, Tent map is applied in enhanced steganalysis population initialization because of its great randomness. Secondly, considering that opposition-based learning can dynamically adjust searching space of enhanced steganalysis population, opposition-based learning via lens imaging strategy is proposed to help enhanced steganalysis escape from local optimal solutions. Then, to reasonably evaluate the detection contribution of steganalysis key feature submodels, the pearson correlation coefficient for steganalysis is designed. On this basis, fitness function is devised to select superior individuals and obtain steganalysis key feature submodels after iteration. It is noted that EIS-OBEA can optimize steganalysis training samples into quite small-size data, so that computational cost can be significantly reduced when maintaining or even improving detection accuracy. Extensive experimental results substantiate that compared with the state-of-the-art peer algorithms, EIS-OBEA not only achieves highly competitive or even better detection performance, but also meliorates steganalysis time-space cost to a large extent.
Author Xu, Lige
Ma, Yuanyuan
Zhang, Yi
Luo, Xiangyang
Zhang, Qianqian
Xin, Xianwei
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Snippet Recent years have witnessed a spurt progress in steganography, which poses challenges for steganalysis. However, previous steganalysis methods attach equal...
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SubjectTerms Accuracy
coefficient for steganalysis
Costs
Data mining
Deep learning
Enhanced steganalysis
evolutionary algorithm
Evolutionary computation
Feature extraction
Lenses
opposition-based learning
Optimization
Steganography
Training
Title EIS-OBEA: Enhanced Image Steganalysis via Opposition-Based Evolutionary Algorithm
URI https://ieeexplore.ieee.org/document/10918830
Volume 20
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